Gökhan ERCANSoftware Development Consultant/Lecturer | NLP Researcher linkedin.com/in/gokhanercan github.com/gokhanercan +90 533 5211430 mail@gokhanercan.com Levent / İSTANBUL www.gokhanercan.com, Birth :1983, Non-smoker.

SKILLS AND INTERESTS

Research Interests Information Retrieval, Natural Language Processing, Machine Learning, SemanticsPhD thesis (ongoing) Semantic Relation Extraction By Enriching Word Embeddings Exploiting Turkish Morphology

Programming Languages C#, Java, Python, Cython, Javascript, C++, CProfessional Expertise SoftwareEngineering,ObjectOrientedDesign&Architecture,WebDevelopment, Unit Testing,

Test Driven Development, Domain Driven Design, Agile Development, Product DevelopmentPersonal Interests Rock Music, Guitar, Keyboard, NLP, Machine Learning, Science, Evolution, Linguistics, Philo-

sophy (Epistemology, Ethics & Morality, Politics)Favourite Authors Karl Popper, Celal Şengör, Richard Dawkins, Yuval Noah Harari, Martin Fowler, Steven Pinker

EDUCATION

2014 - pres. Işık University - Computer Engineering (PhD candidate) (Full scholarship)NLP Thesis : Semantic Relation Extraction By Enriching Word Embeddings Exploiting Turkish Morphology

2010 - 2014 Bogazici University - Software Engineering (MSc)Information Retrieval Project : Adaptive Web Wrappers With Genetic Algorithms

2006 - 2010 Anadolu University - Business Administration2000 - 2006 Yıldız Technical University - Urban Planning (foundation degree)1997 - 2000 Akdeniz College - Science High School

PROFESSIONAL EXPERIENCE

present Software Development Manager, HOSTAMEDIA, İstanbul2007 ∠ Designing and developing corporate development frameworks and products.

∠ Hiring, training andmanaging back-end/front-end software development teams.∠ Developing software development lifecycles and business process workflows.

.NET C# Javascript Web Development Web Crawlers Data Structuring Product Matching Agile SCRUMFramework Development CMS E-Commerce

present Lecturer, IŞıK UNIVERSITY, İstanbul2018 Teaching :

∠ CSE 112 - Object Oriented ProgrammingJava Software Design & Architecture

present Lecturer / Consultant, BT AKADEMI, İstanbul2010 Teaching / Consulting :

∠ Test Driven Development & Unit Testing∠ Enterprise Design Patterns & Architectures∠ Object Oriented Programming Principles & Design Patterns∠ Advanced ASP.NET Development∠ Recently consulted : Akbank, SoftTech, Ericsson, Ziraat Teknoloji, Siemens, Anadolu University, Ga-ranti Teknoloji, Innova, Türk Telekom, Kuveyt Türk Katılım Bankası, Albaraka Türk, Honda, NCR, TÜBİ-TAK Bilgem, Çiçek Sepeti, Belbim, Enerjisa, Intertech, Amerikan Hastanesi

∠ Microsoft Certified Trainer (MCT, MCPD, MCTS, MCP)..NET C# Software Design & Architecture Dependency Injection Software Development Testability Maintainability

2006 Software Developer, HOSTAMEDIA, İstanbul2002 ∠ Developing web content management, e-commerce and hotel reservation system products and so-

lutions.Web Development .NET C# Visual Basic.NET ASP.NET Javascript

PUBLICATIONS

RANLP 2019 Yildiz, O. T., B. Avar, G. Ercan, AN OPEN, EXTENDIBLE, AND FAST TURKISH MORPHOLOGICAL ANALYZER,Recent Advances in Natural Language ProcessingIn this paper, we present a two-level morphological analyzer for Turkish which consists of five main com-ponents : finite state transducer, rule engine for suffixation, lexicon, trie data structure, and LRU cache. Weuse Java language to implement finite state machine logic and rule engine, Xml language to describe thefinite state transducer rules of the Turkish language, which makes the morphological analyzer both easilyextendible and easily applicable to other languages. Empowered with a comprehensive lexicon of 54,000bare-forms including 19,000 proper nouns, our morphological analyzer is amongst the most reliable ana-lyzers produced so far.Lexical Morphology Language Resources Turkish Morphology Morphological Analyzer Open-Source

COLING 2018 G. Ercan, Yildiz, O. T., SEMANTIC MODEL EVALUATION DATASET FOR TURKISH - WORD SIMILARITY ANDRELATEDNESS, The 27th International Conference on Computational Linguistics (Best resource paperaward)In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed toevaluate word similarity and word relatedness tasks while discriminating those two relations from eachother. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two dis-tinct scores for similarity and relatedness.Word-pairs are selected toenable theevaluationof distributionalsemantic models by multiple attributes of words and word-pair relations such as frequency, morphology,concreteness and relation types (e.g., synonymy, antonymy).Distributional Semantics Word Embeddings Word Similarity Word Relatedness Evaluation Dataset Turkish

SİU 2019 Uludogan, G., Özçelik, R., Parlar, S., Ercan, G. and Yıldız, O.T., TÜRKÇE DOĞAL DIL İŞLEME İÇIN ARAYÜZLER,Signal Processing and Communication Applications Conference, 2019, AprilIn this study, we present an online user interface for Turkish Nlptoolkit, which is another Turkish naturallanguage processing tool.Morphological Analysis Morphological Disambiguation Turkish Spell Checker User Interface

UBMK 2018 G. Ercan, O. Erkek, O. Açıkgöz, R. Özçelik, S. Parlar, O.T. Yıldız, MULTIPLE CHOICE EVALUATION DATASETFOR SEMANTIC DISCOURSE AND SENTENCE SIMILARITY TASKS, International Conference on NaturalLanguage and Speech Processing (Original paper in Turkish)Our natural language processing study aims to construct an evaluation dataset which evaluates seman-tic discourse analysis and textual similarity models for the Turkish language at paragraph–sentence, andsentence–sentence levels.Sentence Similarity Discourse Analysis Evaluation Dataset Multiple Choice Exam Questions Turkish

ICNLSP 2018 Yildiz, O. T., K. Ak, G. Ercan, O. Topsakal, C. Asmazoglu, A MULTILAYER ANNOTATED CORPUS FOR TURKISH,International Conference on Natural Language and Speech ProcessingIn this paper, we present the first multilayer annotated corpus for Turkish, which is a low-resourced ag-glutinative language. Our dataset consists of 9,600 sentences translated from the Penn Treebank Corpus.Annotated layers contain syntactic and semantic information including morphological disambiguation ofwords, named entity annotation, shallow parse, sense annotation, and semantic role label annotation.Annotated Corpus Turkish Dataset NER Shallow Parse Sense Annotation

UBMK 2017 Ertopçu, B., Kanburoğlu, A.B., Topsakal, O., Açıkgöz, O., Gürkan, A.T., Özenç, B., Çam, İ., Avar, B., Ercan,G. and Yıldız, O.T., A NEW APPROACH FOR NAMED ENTITY RECOGNITION., In Computer Science andEngineeringIn this study, we design a newmodel for NER algorithms.We train thismodel in our predefined dataset andcompare the results with other models. In the end we get considerable outcomes in a dataset containing1400 sentences.Named Entity Recognition Classification Turkish

UBMK 2017 Açıkgöz, O., Gürkan, A.T., Ertopçu, B., Topsakal, O., Özenç, B., Kanburoğlu, A.B., Çam, İ., Avar, B., Ercan,G. and Yıldız, O.T., ALL-WORDS WORD SENSE DISAMBIGUATION FOR TURKISH., In Computer Science andEngineeringThis paper seeks to address and discuss 6 different feature extraction methods and its classification per-formances using C4.5, RandomForests, Rocchio, Naive Bayes, KNN, Linear andmultilayer perceptron. Thispaper calls into question how the described features performon amorphologically rich language (Turkish)with several classifiers.Word Sense Disambiguation Classification Turkish

UBMK 2017 Topsakal, O., Açıkgöz, O., Gürkan, A.T., Kanburoğlu, A.B., Ertopçu, B., Özenç, B., Çam, İ., Avar, B., Ercan,G. and Yıldız, O.T., SHALLOW PARSING IN TURKISH., In Computer Science and EngineeringIn this study, shallowparsing is appliedonTurkish sentences. These sentences areused to train and test theperformances of various learning algorithmswith various features specified for shallow parsing in Turkish.Shallow Parsing Classification Turkish

AWARDS

Best paper G. Ercan, Yildiz, O. T., SEMANTIC MODEL EVALUATION DATASET FOR TURKISH - WORD SIMILARITY ANDRELATEDNESS, The 27th International Conference on Computational Linguistics (COLING 2018)Best resource paper award

REWIEVER

SİU 2020 28th Signal Processing and Communications Applications ConferenceAI Review 2019 Artificial Intelligence Review - An International Science and Engineering Journal

SİU 2019 27th Signal Processing and Communications Applications Conference

LANGUAGES

Turkish (native) ○ ○ ○ ○ ○English ○ ○ ○ ○ ○

+ ORGANIZATIONS

∠ T90 Turkey Blockchain Consortium (BoardMember)[2018]

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